International Journal of Communication Systems最新文献

Underwater wireless sensor networks (UWSNs) based on autonomous underwater vehicles (AUVs) have become the standard technology for underwater search tasks with the advent of new underwater information and communication technologies (ICTs). It is also vulnerable to threats and malicious attacks because of its inherent characteristics, open acoustic channel and hostile underwater environment. The purpose of this research is to develop a secure data transfer (SDF) system based on software-defined networking (SDN) architecture with cooperative searching scheme (CSS) (SDN-SDF-CSS) for AUV-based UWSN. This part employs data, local control and the primary control layer to provide a scalable SDN-based architecture for the AUV-based UWSN. To accomplish the underwater search operation, the data layer is primarily deployed to gather AUVs (G-AUVs) and store AUVs (S-AUVs). In order to schedule the AUV, the clustering process takes place based on priority ranking with respect to the average power of each cluster. Then, the CSS is developed, and it is performed in the data layer. The hierarchical localization framework (HLF) can be used to locate each AUV's location within the data layer, which is a necessary step in deploying the cooperative searching model. Finally, for an efficient data transfer, the communication model is deployed in the data layer. UWSNs are vulnerable to various malicious attacks (such as bad-mouthing attacks, on–off attacks, blackhole attacks and wormhole attacks) because of the high bit error rate and unstable optical/acoustic channels in the underwater environment. To overcome this, an SDF technique is used, which involves isolating the attacker node from the data layer. The suggested SDN-SDF-CSS model is implemented on the MATLAB platform, and its performance is evaluated using various evaluation metrics, both with and without attacks. As a result, the proposed SDN-SDF-CSS model has achieved better performance and proved its superiority in the UWSN environment.

Traditional Orthogonal Multiple Access (OMA) and spectrum sharing methods struggle to provide the diverse quality of service (QoS) demands for enhanced mobile broadband (eMBB), ultra-reliable low latency communications (uRLLC), and massive machine type communications (mMTC) leading to suboptimal performance and service quality degradation. Single-carrier-non-orthogonal multiple access (SC-NOMA) appears to be a more optimized solution. It can serve multiple users simultaneously on the same time-frequency resources. This approach offers both enhanced spectrum efficiency and meets the QoS requirements for the coexistence of eMBB, uRLLC, and mMTC. However, SC-NOMA has some drawbacks. Decoding a user's signal involves a complex successive interference cancellation (SIC) process that gets harder with more users causing delays and errors. Additionally, strong user signals can interfere with weaker ones, limiting the number of users per channel. In order to overcome the drawbacks associated with OMA and SC-NOMA, this paper introduces a new method called user-paired NOMA (hybrid NOMA). Hybrid NOMA adopts a strategic approach, employing two user pairing techniques: near-far/far-near (NF-FN) and near-near/far-far (NN-FF). NF-FN pairing prioritizes users with similar signal strengths but different distances from the base station. This minimizes interference for the weaker user during SIC. NN-FF pairing, on the other hand, groups users with similar signal strengths and proximity. This approach further simplifies SIC and minimizes potential interference altogether. The simulation results demonstrate trade-offs between eMBB and uRLLC performance. OMA suffers with dedicated resource allocation, while SC-NOMA balances performance but experiences interference. NN-FF prioritizes eMBB and offers best latency, while NF-FN prioritizes uRLLC with high spectral efficiency but suffers from higher latency. Finally, by providing a thorough grasp of how hybrid NOMA resource allocation works to improve the performance of various use cases, this research makes a significant contribution to the field of 5G spectrum optimization.

In today's intricate wireless communication environment, ensuring system quality demands the use of a reliable and versatile antenna system. This research article introduces a polarization reconfigurable antenna integrated with a 4 × 4 Artificial Magnetic Conductor (AMC) surface. The AMC unit cell exhibits a triple-band reflection phase response at 1.8GHz, 4.5GHz, and 5.5GHz, demonstrating double negative metamaterial behavior. The antenna features two distinct C-shaped metal strips connected to two PIN diodes, enabling dynamic current distribution adjustment. Consequently, the proposed antenna offers three reconfigurable states, facilitating seamless switching between dual circular polarization (left and right-hand circular polarization) and linear polarization. With a frequency coverage ranging from 1.29 to 2.52GHz and 3.59 to 6.15GHz, the antenna boasts a maximum axial ratio (AR) bandwidth of 31.96%. Additionally, it achieves a maximum peak gain of 5.5 dB and maintains front-to-back ratio (FBR) values exceeding 25 dB, while recording a minimum specific absorption rate (SAR) value of 0.1059 W/kg. The integration of the AMC surface ensures enhanced performance of the antenna. Experimental results from constructed prototypes closely align with simulation outcomes, validating the effectiveness of the proposed antenna. Consequently, this antenna holds significant promise for next-generation wireless applications.

A low profile, concave conformal ring cylindrical dielectric resonator antenna (CDRA) employing frequency selective surface (FSS) using split ring resonator (SRR) for wideband and high gain applications is presented. The ring CDRA loaded with the monopole is designed to excite the TM_01δ mode to increase the antenna's bandwidth. The effect of a curved ground plane (GP) on the radiation performance of CDRA is studied. A 5 × 5 array of the SRR is placed above the conformal GP at a far-field distance optimized to (2n ± 1) λ/4 from the radiating element to enhance the gain of the proposed structure. The planar CDRA with FSS is compared with the conformal CDRA with FSS and a 4.3 GHz improvement in bandwidth is observed due to the multiple reflection and surface reflection leading to a 3.2 dBi improvement in gain. An impedance bandwidth of 51.8% (5 to 8.5 GHz) with a maximum gain of 8.7 dBi at 7.3 GHz resonant frequency and 99% radiation efficiency at 5.9 GHz is offered by the proposed antenna with FSS. Additionally, the proposed CDRA has a low profile of 0.12 λ₀ where λ₀ is the lower cut-off frequency's wavelength. A good agreement is observed between the simulated and measured results.

Massive multiple-input multiple-output (MA-MIMO) has been hailed as an auspicious technology for the future generation of wireless communications because it can considerably increase the capacity of the communication network. However, using the maximum likelihood (ML) direction-of-arrival (DOA) estimate method is severely constrained in actual systems because of the computationally expensive multi-dimensional searching procedure. This paper proposes a novel approach to estimate DOA and channels by incorporating deep learning into the MA-MIMO system. Here, a deep belief network (DBN) is used to learn both the spatial structures in the angle domain and the statistics of the wireless channel through both online and offline learning procedures. Also, a bald eagle search (BES) Optimization is used along with DBN to attain high precision through optimal training. The proposed model can estimate the channel based on the predicted DOA and the complex gain. According to numerical results, the suggested method performs significantly better than state-of-the-art methods, particularly in tough conditions like low signal-to-noise ratio (SNR) and a finite number of snapshots. The proposed DBN-BES technique accomplishes less root mean square error (RMSE) as 0.01 for SNR of 5 dB in elevation calculation and 0.02 for SNR of 5 dB in azimuth calculation. Also, the proposed algorithm greatly reduces computational complexity.

Wireless sensor networks (WSNs) are becoming increasingly important and well liked for delivering pervasive computing environments for a range of applications. Extending the networking life lifetime in WSNs is an important issue that must be addressed. Effective techniques for conserving the WSN's limited energy resources must be developed. Cross-layer protocols are employed in WSNs to solve network lifespan difficulties. This paper proposes a new cross-layer–cross-domain routing scheme with stages such as “(1) network association stage, (2) nearer node detection phase, and (3) consistent state phase.” In the consistent stage, the optimal cluster head selection (CHS) is carried out by taking into account risk, delay, energy, trust, and distance. A new model called manta ray collided dwarf mongoose optimization (MRC-DMO) is introduced to help with this. Furthermore, the routing is accomplished by dependable data communication. The results obtained establish the effectiveness of the MRC-DMO scheme for SEACRCLCD in WSN over varied methods.

New cognitive radio (CR) systems require high throughput and bandwidth. Hence, CR users need to detect wide frequency bands of the radio spectrum to exploit unused frequency channels. This paper proposes a new wideband spectrum sensing (WBSS) detection approach based on machine learning (ML) for scanning subchannels. The originality of the proposed approach is to detect spectrum opportunities using a narrowband spectrum sensing (NBSS) method-based support vector machine (SVM) classification and two features: energy and goodness of fit (GoF). The simulation results show that the proposed WBSS approach-based ML presents a higher probability of detection than the WBSS approach-based conventional detectors, even at low signal-to-noise ratio (SNR). Finally, the software defined radio (SDR) implementation validates the proposed WBSS approach for real detection scenarios.

The latest developments in the industrial Internet of things (IIoT) have opened up a collection of possibilities for many industries. To solve the massive IIoT data security and efficiency problems, a potential approach is considered to satisfy the main needs of IIoT, such as high throughput, high security, and high efficiency, which is named blockchain. The blockchain mechanism is considered a significant approach to boosting data protection and performance. In the quest to amplify the capabilities of blockchain-based IIoT, a pivotal role is accorded to the Glowworm Swarm Optimization (GSO) algorithm. Inspired by the collaborative brilliance of glowworms in nature, the GSO algorithm offers a unique approach to harmonizing these conflicting aims. This paper proposes a new approach to improve the performance optimization of blockchain-based IIoT using the GSO algorithm due to the blockchain's contradictory objectives. The proposed blockchain-based IIoT system using the GSO algorithm addresses scalability challenges typically associated with blockchain technology by efficiently managing interactions among nodes and dynamically adapting to network demands. The GSO algorithm optimizes the allocation of resources and decision-making, reducing inefficiencies and bottlenecks. The method demonstrates considerable performance improvements through extensive simulations compared to traditional algorithms, offering a more scalable and efficient solution for industrial applications in the context of the IIoT. The extensive simulation and computational study have shown that the proposed method using GSO considerably improves the objective function and blockchain-based IIoT systems' performance compared to traditional algorithms. It provides more efficient and secure systems for industries and corporations.

Reconfigurable fractal antenna (RFA) design is always necessary for the continued development of wireless communication systems as the antenna is playing a vital part in device performance. The need for efficient radiators that are compact, low-profile, inexpensive, and low weight has attracted scientists for their research works. As a result, numerous ideas were put forward by the researchers to attempt to resolve these problems by utilizing various kinds of fractal and reconfigurable antennas as well as their combinations. However, it is challenging to have a clear and transparent review of the various works with a large variety of solutions and their uniqueness. The advanced RFA design for wireless applications is reviewed in this study together with its most recent and pertinent counterparts. In Koch RFA, bandwidths enable selective bands spanning 1–6 GHz frequency range. Further, in band RFA design, the overall bandwidth ranges from 1.45 to 4.52 GHz (103%) with low gain; on the other hand, the crescent RFA achieves 5.67 dBi peak gain. Furthermore, in polarization RFA, the ARBWs are 17.61% (2.2–2.62 GHz) and 8.69% (2.91–3.18 GHz). The Hilbert RFA operates at 0.9 and 2.45 GHz with gains of 3.1 and 7 dBi, respectively. This article investigates a comprehensive review of the requirements for RFAs for wireless applications. Furthermore, a comparative study on different reconfigurability with switching techniques, fractal geometries, various RFA design approaches to enhance device performance, and their significance is discussed. Existing research challenges and future directions are also discussed as part of this article.